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          <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;咱们蓝翔如果不踏踏实实学本事，那跟清华北大还有什么区别？—— by 蓝翔校长</p>
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        <h2 class="green">Principal Components Analysis</h2>
        <p class="exp">by Meta</p>
    
 <div class="yue top20">
    <p>&nbsp;&nbsp;&nbsp;&nbsp;PCA（Principal Component Analysis）是一种常用的数据分析方法。PCA通过线性变换将原始数据变换为一组各维度线性无关的表示，可用于提取数据的主要特征分量，常用于高维数据的降维。</p>    
   <h2>算法及实例</h2>
<pre><code>设有m条n维数据。   
1）将原始数据按列组成n行m列矩阵X   
2）将X的每一行（代表一个属性字段）进行零均值化，即减去这一行的均值   
3）求出协方差矩阵C=1/mXXT
4）求出协方差矩阵的特征值及对应的特征向量   
5）将特征向量按对应特征值大小从上到下按行排列成矩阵，取前k行组成矩阵P 
6）Y=PX即为降维到k维后的数据
</code></pre>
<h3>有待补充。。。</h3> 	   
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